17972

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques

Grigori Fursin, Anton Lokhmotov, Dmitry Savenko, Eben Upton
dividiti, UK
arXiv:1801.08024 [cs.HC], (19 Jan 2018)

@article{fursin2018collective,

   title={A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques},

   author={Fursin, Grigori and Lokhmotov, Anton and Savenko, Dmitry and Upton, Eben},

   year={2018},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.HC}

}

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Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer. Apart from the ever growing design and optimization complexity, there exist even more fundamental problems such as lack of interdisciplinary knowledge required for effective software/hardware co-design, and a growing technology transfer gap between academia and industry. We introduce our new educational initiative to tackle these problems by developing Collective Knowledge (CK), a unified experimental framework for computer systems research and development. We use CK to teach the community how to make their research artifacts and experimental workflows portable, reproducible, customizable and reusable while enabling sustainable R&D and facilitating technology transfer. We also demonstrate how to redesign multi-objective autotuning and machine learning as a portable and extensible CK workflow. Such workflows enable researchers to experiment with different applications, data sets and tools; crowdsource experimentation across diverse platforms; share experimental results, models, visualizations; gradually expose more design and optimization choices using a simple JSON API; and ultimately build upon each other’s findings. As the first practical step, we have implemented customizable compiler autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi 3 devices, reduced the execution time and code size by up to 40%, and applied machine learning to predict optimizations. We hope such approach will help teach students how to build upon each others’ work to enable efficient and self-optimizing software/hardware/model stack for emerging workloads.
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